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Models with limited dependent variables. Doctoral Program 2006-2007 Katia Campo. 3. Nested Logit Model. (K.Train, Ch.4, Franses and Paap Ch.5). 3. Nested Logit Model. Choice between J>2 alternatives which can be grouped into subsets based on differences in substitution pattern Example:.

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Models with limited dependent variables

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## Models with limited dependent variables

Doctoral Program 2006-2007

Katia Campo

## 3. Nested Logit Model

(K.Train, Ch.4, Franses and Paap Ch.5)

### 3. Nested Logit Model

• Choice between J>2 alternatives which can be grouped into subsets based on differences in substitution pattern

• Example:

Choice of transportation mode

Car

Transit

Carpool

Bus

Train

Car alone

### 3. Nested Logit Model

• Assumption cumulative distribution of error terms

• Probability

### 3. Nested Logit Model

• Probability can be decomposed into 2 logit models

(1)

(2)

### 3. Nested Logit Model

• Link between Pni | Bk * Pn Bk (upper and lower model level) through inclusive value Ink

• To comply with RUM k must be in the [0,1] interval

• IIA within, not across nests

### 3. Nested Logit Model

Estimation

• Joint estimation

• Sequential estimation

• Estimate lower model

• Compute inclusive value

• Estimate upper model with inclusive value as explanatory variable

• Add fourth step, using the parameter estimates as starting values for joint estimation

### 3. Nested Logit Model

• Example: Location choices by French firms in Eastern and Western Europe

• Dependent var.: probability of choosing location j Pj=P(πj> πk  k≠j )

• Location choices are likely to have a nested structure (non-IIA)

• First, select region (East or Western Europe)

• Next, select country within region

Disdier and Mayer (2004)

### 3. Nested Logit Model

• Example 1: Location choices by French firms in Eastern and Western Europe

Location choice

E.Eur

W.Eur

………

………

CN

C1

CJ+1

CJ

Disdier and Mayer (2004)

### 3. Nested Logit Model

• Location choices: Data

• 1843 location decisions in Europe from 1980 to 1999 (official statistics)

• 19 host countries (13 W.Eur, 6 E.Eur)

Disdier and Mayer (2004)

### 3. Nested Logit Model

• Location choices: Data

• NFFrench firms already located in the country

• GDPGDP

• GPP/CAPGDP per capita

• DISTDistance France – host country

• WAverage wage per capita (manufacturing)

• UNEMPLunemployment rate

• EXCHRExchange rate volatility

• FREEFree country

• PNFREEPartly free and not free country

• PR1Country with political rights rated 1

• PR2Country with political rights rated 2

• PR345Country with political rights rated 3,4,5

• PR67Country with political rights rated 6,7

• LIAnnual liberalization index

• CLICumulative liberalization index

• ASSOC=1 if an association agreement is signed

Disdier and Mayer (2004)

3. Nested Logit Model

Disdier and Mayer (2004)

3. Nested Logit Model

Disdier and Mayer (2004)

3. Nested Logit Model

• Example 2: Shopping centre choice

3. Nested Logit Model

• Example 2: Shopping centre choice

3. Nested Logit Model

• Example 2: Shopping centre choice

### 3. Nested Logit Model

• (Purchase) incidence and choice models can be linked in the same way

Include incl.value 

Purchase decision

No purchase

Purchase

...

(See above: Bucklin & Gupta)

Altern. J

Altern.1

### 3. Nested Logit Model

• Other examples

• Private label versus nationals brands

• Same versus other brand

• Fixed rate (low risk) versus variable rate (high risk) investments

• Choice of transportation mode

• ....

## 4. Probit Model

(K.Train, Ch.5 ; Franses and Paap, Ch.4-5)

### 4. Probit Model

• Based on the general RUM-model

• Ass.: error terms are distributed normal with a mean vector of zero and covariance matrix 

• density function:

### 4. Probit Model

Logit or Probit?

• Trade-off between tractability and flexibility

• Closed-form expression of the integral for Logit, not for Probit models

• Probit allows for random taste variation, can capture any substitution pattern, allows for correlated error terms and unequal error variances

 Dependent on the specifics of the choice situation

### 4. Probit Model

Estimation

• Approximation of the multidimensional integral

• Non-simulation procedures (see Kamakura 1989)

Can usually only be applied to restricted cases and/or provide inaccurate estimations

• Simulation procedures (see Geweke et al.1994, Train)

### 4. Probit Model

Simulation-based estimation(binary probit, CFS)

• Step 1

• For each observation n=1, ..., N draw r ~ N(0,1), (r = 1, ......., R: repetitions)

• Initialize y_count= 0, =mt (starting values)

• Compute y*rn = xn mt + L r ; L= choleski factor (LL’= )

• Evaluate: y*rn >0  y_count= y_count+1

• Repeat R times

Weeks (1997)

### 4. Probit Model

Simulation-based estimation(binary probit, CFS)

• Step 2: calculate probabilities

Pn| mt= y_count/R

• Step 3: Form the simulated LL function

SLL=  n yn ln(Pn|mt)+(1-yn) ln(1-Pn|mt)

• Step 4: Check convergence criteria (SLL(mt)- SLL(mt-1))

• Step 5: Update mt: mt+1 = mt + v

• Step 6: Iterate (until convergence)

Weeks (1997)

### 4. Probit Model

Simulation-based estimation: MNProbit

• Based on same principles

• More efficient simulation procedures (see Train)

• Identification: normalization of level and scale

• Re-express model in utility differences

• Normalization of varcov matrix (see Train)

### 4. Probit Model

• Random taste variation

• Model with random coefficients

• E.g.:n~N(b,W)

• Unj = b’xnj+ *’n xnj + nj

• = b’xnj+ nj

• nj : correlated error terms (dep.on xnj, see Train)

### 4. Probit Model

• Substitution patterns

• Full covariance matrix (no parameter restrictions) unrestricted substitution patterns

• Structured covariance matrix (restrictions on some covariance parameters)

•  the structure imposed on  determines the substitution pattern and may allow to reduce the number of parameters to be estimated

### 4. Probit Model

• Example (Kamakura and Srivastava 1984): random utility components ni, nj are more (less) highly correlated when i and j are more (less) similar on important attributes

(dij = weighted eucledian distance between i & j)

### 4. Probit Model

• Examples

• Choice models at brand-size level: correlation between ≠ sizes of same brand (Chintagunta 1992)

MNL model

gives biased

estimates

of price

elasticity

### 4. Probit Model

• Examples

• Firm innovation (Harris et al. 2003)

• Binary probit model for innovative status (innovation occurred or not)

• Based on panel data  correlation of innovative status over time: unobserved heterogeneity related to management ability and/or strategy

Model (2)-(4) account for unobserved heterogeneity (ρ) -> superior results

### 4. Probit Model

• Examples

• Dynamics of individual health (Contoyannis, Jones and Nigel 2004)

• Binary probit model for health status (healthy or not)

• Survey data for several years  correlation over time (state dependence) + individual-specific (time-invariant) random coefficient

### 4. Probit Model

• Examples

• Choice of transportation mode (Linardakis and Dellaportas 2003)  Non-IIA substitution patterns

## 5. Ordered Logit Model

### 5. Ordered Logit Model

• Choice between J>2 ordered ‘alternatives’

• Ordinal dependent variable y = 1, 2, ... J, with

rank(1) < rank(2) < ... < rank(J)

• Example:

• Purchase of 1, 2, ... J units

• Evaluation on a J-point scale ranging from, e.g., ‘dislike very much’ to ‘like very much’

### 5. Ordered Logit Model

• Suppose yi* is a continous latent variable which

• is a linear function of the explanatory variables

yn* = Xn + n

• and can be ‘mapped’ on an ordered multinomial variable as follows:

yn= 1 if 0 < yn*  1

yn= j if j-1 < yn*  j

yn= J if J-1 < yn*  J

0 < 1 < …. < j < … < J

### 5. Ordered Logit Model

Ordered logit (see above)

• 0 , J and 0: set equal to zero

### 5. Ordered Logit Model

Interpretation of parameters (marginal effects)

Estimation: ML

### 5. Ordered Logit Model

• Assumption of equal slope k

• Biased estimates if assumption of strictly ordered

outcomes does not hold

• treat outcomes as nonordered unless there are

good reasons for imposing a ranking

### 5. Ordered Logit Model

Example: Effectiveness of better public transit as a way to reduce automobile congestion and air polution in urban areas

• Research objective: develop and estimate models to measure how public transit affects automobile ownership and miles driven.

• Data: Nationwide Personal Transportation Survey (42.033 hh): socio-demo’s, automobile ownership and use, public transportation avail.

Kim and Kim (2004)

### 5. Ordered Logit Model

• Dependent variable ownership model = number of cars (k = 0, 1, 2,  3)  ordinal variable

• C*i = latent variable: automobile ownership propensity of hh i

• Relation to observed automobile ownership:

Ci=k if k-1 < ’xi +  < k

• P(Ci=k)=F(k- ’xi) - F(k-1 - ’xi)

Kim and Kim (2004)

5. Ordered Logit Model

5. Ordered Logit Model

5. Ordered Logit Model

5. Ordered Logit Model

• Examples

• Occupational outcome as a function of socio-demographic characteristics (Borooah)

• Unskilled/semiskilled

• Skilled manual/non-manual

• Professional/managerial/technical

• School performance (Sawkins 2002)

• Function of school, teacher and student characteristics

• Level of insurance coverage

### D.Heterogeneity

• Observed heterogeneity

• Unobserved heterogeneity

• Over decision makers

• Random coefficients Models

• E.g. Mixed Logit Model (see Train)

• Over segments

• Latent class estimation

### D.Heterogeneity: Latent class est.

• Ass.: Consumers can be placed into a small number of – homogeneous - segments which differ in choice behavior ( response parameters)

• Relative size of the segment s (s=1, 2, ..., M) is given by

fs = exp(s) / s’exp(s’)

Kamakura and Russell (1989)

### D.Heterogeneity: Latent class est.

• Probability of choosing brand j, conditional on consumer i being a member of segment s

Ps(yn = j|Xn) = exp(Xjns)

lexp(Xlns)

### D.Heterogeneity: Latent class est.

• Unconditional probability that consumer i will choose brand j

P(yn = j|Xn) = sfs Ps(yn = j|Xn)

= s exp(s) exp(Xjns)

s’exp(s’) lexp(Xlns)

### D.Heterogeneity: Latent class est.

• Estimation: Maximum Likelihood

• Likelihood of a hh’s choice history Hn

L(Hn) = s [ exp(s)L(Hn|s) / s’ exp(s’) ]

with

L(Hn|s) = t Ps(ynt = c(t) | Xnt)

c(t) = index of the chosen option at time t.

• Maximize likelihood over all hh’s: n L(Hn)

### D. Heterogeneity: Latent class est.

Segment analysis

• Based on parameter estimates (e.g. difference in price sensitivity)

• Based on segment profiles

• Post-hoc: based on assignment of hh to segments; Probability that hh n belongs to segment s =

P(ns | Hn) = L(Hn|s)fs / s’ [L(Hn|s’)fs’]

Analyze characteristics of different segments

• A priori: make fs a function of variables that may explain segment membership (e.g. income for segments which differ in price sensitivity)

### D.Heterogeneity: Latent class est.

• Example: heterogeneity in price sensitivity (Bucklin and Gupta 1992)

### D.Heterogeneity: Latent class est.

• Example: heterogeneity in price sensitivity

### D.Heterogeneity: Latent class est.

Choice segments: segment 1 = more sensitive to price and promo

Incidence segments: segment 2 and 4 = more sensitive to changes

in category attractiveness (change in price/promo)

 Confirms that  combinations of choice/inc.price sensitivity occur

### D. Heterogeneity: Latent class est.

• Segment analysis: price elasticity

### D. Heterogeneity: Latent class est.

• Segment analysis: socio-demographic profile